Papers with Code 아카이브
Unsupervised Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Unsupervised Domain Adaptation is a learning framework to transfer knowledge learned from source domains with a large number of annotated training examples to target domains with unlabeled data only.
Source: Domain-Specific Batch Normalization for Unsupervised Domain Adaptation
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/phymhan/udacu
Unsupervised Domain Adaptation via Calibrating Uncertainties
https://github.com/pikkaay/efficientnet_gpu
https://github.com/pjlab-adg/3dtrans
Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection
https://github.com/pmirallesr/DRCN-Torch
Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation
https://github.com/pmorerio/minimal-entropy-correlation-alignment
Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation
https://github.com/priyavrat-misra/xrays-and-gradcam
https://github.com/pszemraj/BoulderAreaDetector
https://github.com/pwc-1/Paper-9/tree/main/5/vit_hybrid
Learning CNN on ViT: A Hybrid Model to Explicitly Class-specific Boundaries for Domain Adaptation
https://github.com/pwc-1/Paper-9/tree/main/6/Alexnet-ABeffect/test50_feature
https://github.com/pytorch/vision
https://github.com/qliu24/render-3d-segmentation
Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles
https://github.com/qubvel/efficientnet
https://github.com/qubvel/resnet_152
https://github.com/raghakot/keras-resnet
https://github.com/rahul-t-p/ASVspoof-2019
https://github.com/rahulchaurasiya1/cautious-waddle
https://github.com/rajneeshaggarwal/google-efficientnet
https://github.com/realmichaelye/Skin-Lesions-Classifier-ResNet50
https://github.com/researchmm/SiamDW
https://github.com/rickyHong/tfRecord-Caltech256-repl2
https://github.com/rishabmallick/Sign-language-live-predictor
https://github.com/robertofranceschi/Image-classification-on-Caltech101-using-CNNs
https://github.com/rpryzant/proxy-a-distance
https://github.com/rtanno21609/AdaptiveNeuralTrees
https://github.com/ry/tensorflow-resnet
https://github.com/saikrishnadas/ResNet-Pytorch
https://github.com/saloni1998/Cifar-10
https://github.com/sammyamajumdar/ResNet50
https://github.com/sangHa0411/CIFAR100
https://github.com/sangHa0411/ResNetCIFAR100
https://github.com/sangdon/pac-ps-w
https://github.com/sanghyun-son/clustering-kernels
https://github.com/sanoyo/dl-model
https://github.com/sayakpaul/AdaMatch-TF
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
https://github.com/sayakpaul/Adaptive-Gradient-Clipping
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.